Prediction of Reduced Ion Mobility Constants from Structural Information Using Multiple Linear Regression Analysis and Computational Neural Networks

Wessel, Matthew D.; Jurs, Peter C.

doi:10.1021/ac00087a012

Cited by 89 publications

(94 citation statements)

References 33 publications

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“…The CNNs used for this analysis are three-layered, fully connected, feed-forward networks, and they have been described in detail by Jurs and coworkers [41,42]. The number of neurons of the input layer corresponds to the number of descriptors in the model.…”

Section: Cnn Methods (Adapt)mentioning

confidence: 99%

“…That is, CNNs that produce training and cross-validation set errors that are low and similar in magnitude tend to perform well in predicting the properties of interest for compounds not used in the training process. A quasi-Newton method BFGS (Broyden -Flectcher -Golfarb -Shanno) [42] is used to train the network. It should be noted that the ratio of training set observations to adjustable parameters should be kept above 2.0 to avoid overtraining [43].…”

Section: Cnn Methods (Adapt)mentioning

confidence: 99%

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QSPR Prediction of pK_a for Benzoic Acids in Different Solvents

2008

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A Computational Neural Network (CNN) derived model is proposed for the pK a prediction of benzoic acids in different solvents. The system studied contains 519 pK a values corresponding to 136 benzoic acids determined in water and in 8 organic solvents. The benzoic acids were described by the usual molecular descriptors and the solvents by a number of physical properties and by several parameters of the most widely used polarity solvent scales. The model is composed of seven descriptors -five of them corresponding to the solute and the other two to the solvents -and was validated by an external prediction set. The three sets of values needed in the analysis, training, prediction, and cross-validation, have the same squared correlation coefficient (0.998) and Root-Mean Square Error (RMSE) (0.21) values. The robustness of the model is also given for the statistical results for small subsets, such as ortho-and non-ortho-substituted acids, those obtained in protic or aprotic solvents, those obtained in each solvent, and even those for the set of pK a values of one specific acid in several solvents. The descriptors encode information about the chemical nature of the solutes and the solvents that is clearly related to the interactions present in the dissociation process in solution. The derived model also has the ability to predict pK a values of a larger set of aromatic neutral acids, containing both phenols and benzoic acids.

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Section: Cnn Methods (Adapt)mentioning

confidence: 99%

Section: Cnn Methods (Adapt)mentioning

confidence: 99%

QSPR Prediction of pK_a for Benzoic Acids in Different Solvents

2008

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“…Whilst many approaches to IMS explicitly use or attempt to derive information on the 3D structure of 1217 the ion another approach is to use molecular descriptors to adequately describe an ion and predict 1218 the reduced mobility without any requirement to carry out computationally expensive geometry 1222 (Wessel and Jurs, 1994). Later, using six molecular descriptors on a training set of 135 compounds…”

mentioning

confidence: 99%

Ion mobility spectrometry‐mass spectrometry (IMS‐MS) of small molecules: Separating and assigning structures to ions

Lapthorn

Pullen

Chowdhry

2012

Mass Spectrometry Reviews

346

275

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The phenomenon of ion mobility (IM), the movement/transport of charged particles under the influence of an electric field, was first observed in the early 20th Century and harnessed later in ion mobility spectrometry (IMS). There have been rapid advances in instrumental design, experimental methods, and theory together with contributions from computational chemistry and gas‐phase ion chemistry, which have diversified the range of potential applications of contemporary IMS techniques. Whilst IMS‐mass spectrometry (IMS‐MS) has recently been recognized for having significant research/applied industrial potential and encompasses multi‐/cross‐disciplinary areas of science, the applications and impact from decades of research are only now beginning to be utilized for “small molecule” species. This review focuses on the application of IMS‐MS to “small molecule” species typically used in drug discovery (100–500 Da) including an assessment of the limitations and possibilities of the technique. Potential future developments in instrumental design, experimental methods, and applications are addressed. The typical application of IMS‐MS in relation to small molecules has been to separate species in fairly uniform molecular classes such as mixture analysis, including metabolites. Separation of similar species has historically been challenging using IMS as the resolving power, R, has been low (3–100) and the differences in collision cross‐sections that could be measured have been relatively small, so instrument and method development has often focused on increasing resolving power. However, IMS‐MS has a range of other potential applications that are examined in this review where it displays unique advantages, including: determination of small molecule structure from drift time, “small molecule” separation in achiral and chiral mixtures, improvement in selectivity, identification of carbohydrate isomers, metabonomics, and for understanding the size and shape of small molecules. This review provides a broad but selective overview of current literature, concentrating on IMS‐MS, not solely IMS, and small molecule applications. © 2012 Wiley Periodicals, Inc., Mass Spec Rev 32:43–71, 2013

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“…The CNNs used are three-layer, fully connected, feed-forward networks, that were employed in our previous papers on the pK a estimation of phenols [4] and benzoic acids [5], and have been described in detail by Jurs and coworkers [60,61]. The number of neurons of the input layer corresponds to the number of descriptors in the model.…”

Section: Cnn Methods (Adapt)mentioning

confidence: 99%

QSPR Prediction of pK for Aliphatic Carboxylic Acids and Anilines in Different Solvents

Bosque

Sales

2008

QSAR Comb. Sci.

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Computational Neural Network (CNN)-based QSPR methodology is applied to predict the pK a of aliphatic carboxylic acids and pK b of anilines in protic and aprotic solvents. The proposed non-linear models contain seven descriptors, five of them are of the usual molecular type corresponding to the solutes and the other two describe the solvent as a bulk. The descriptors of both models are similar, and comparable to those obtained in previous studies for phenols and benzoic acids. The statistical results of the correlations, for the training, prediction, and validation sets of the two families are very good, with R 2 % 0.98 and rms error % 0.3 pK units. The information encoded in the descriptors allows an interpretation of the dissociation process studied based on the specific and non-specific solute -solvent interactions.

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Prediction of Reduced Ion Mobility Constants from Structural Information Using Multiple Linear Regression Analysis and Computational Neural Networks

Cited by 89 publications

References 33 publications

QSPR Prediction of pK_a for Benzoic Acids in Different Solvents

QSPR Prediction of pK_a for Benzoic Acids in Different Solvents

Ion mobility spectrometry‐mass spectrometry (IMS‐MS) of small molecules: Separating and assigning structures to ions

QSPR Prediction of pK for Aliphatic Carboxylic Acids and Anilines in Different Solvents

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Prediction of Reduced Ion Mobility Constants from Structural Information Using Multiple Linear Regression Analysis and Computational Neural Networks

Cited by 89 publications

References 33 publications

QSPR Prediction of pKa for Benzoic Acids in Different Solvents

QSPR Prediction of pKa for Benzoic Acids in Different Solvents

Ion mobility spectrometry‐mass spectrometry (IMS‐MS) of small molecules: Separating and assigning structures to ions

QSPR Prediction of pK for Aliphatic Carboxylic Acids and Anilines in Different Solvents

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Product

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QSPR Prediction of pK_a for Benzoic Acids in Different Solvents

QSPR Prediction of pK_a for Benzoic Acids in Different Solvents